Mastering SQL programming skills is fundamental in computer science education, and Online Judging Systems (OJS) play a critical role in automatically assessing SQL codes, improving the accuracy and efficiency of evaluations. However, these systems are vulnerable to manipulation by students who can submit "cheating codes" that pass the…

Assessing students' answers and in particular natural language answers is a crucial challenge in the field of education. Advances in transformer-based models such as Large Language Models (LLMs), have led to significant progress in various natural language tasks. Nevertheless, amidst the growing trend of evaluating LLMs across diverse tasks,…

The current educational system relies heavily on manual grading, posing challenges such as delayed feedback and grading inaccuracies. Automated grading tools (AGTs) offer solutions but come with limitations. To address this, "GRAD-AI" is introduced, an advanced AGT that combines automation with teacher involvement for precise grading,…

This study investigated the impact of AI-powered adaptive testing on student academic performance and test anxiety, comparing its effectiveness to traditional testing methods. Using a quantitative research approach, hierarchical regression analysis was employed to examine the influence of adaptive testing on student outcomes, controlling for…

Recent developments in the use of large-language models have led to substantial improvements in the accuracy of content-based automated scoring of free-text responses. The reported accuracy levels suggest that automated systems could have widespread applicability in assessment. However, before they are used in operational testing, other aspects of…

Assessing the difficulty of reading comprehension questions is crucial to educational methodologies and language understanding technologies. Traditional methods of assessing question difficulty rely frequently on human judgments or shallow metrics, often failing to accurately capture the intricate cognitive demands of answering a question. This…

The pervasive issue of cheating in educational tests has emerged as a paramount concern within the realm of education, prompting scholars to explore diverse methodologies for identifying potential transgressors. While machine learning models have been extensively investigated for this purpose, the untapped potential of TabNet, an intricate deep…

The launch of ChatGPT and the rapid proliferation of generative AI (GenAI) have brought transformative changes to education, particularly in the field of assessment. This has prompted a fundamental rethinking of traditional assessment practices, presenting both opportunities and challenges in evaluating student learning. While numerous studies…

Contribution: This article suggests a novel method for diagnosing a learner's cognitive proficiency using deep neural networks (DNNs) based on her answers to a series of questions. The outcome of the forecast can be used for adaptive assistance. Background: Often a learner spends considerable amounts of time in attempting questions on the concepts…

The purpose of this study was to gauge the attitudes towards artificial intelligence (AI) use in the science classroom by science teachers at the start of generative AI chatbot popularity (March 2023). The lens of distributed cognition afforded an opportunity to gather thoughts, opinions, and perceptions from 24 secondary science educators as well…

Online learning has steadily increased since well before the COVID-19 pandemic (Seaman et al., 2018), but research has yet to explore online students' perceptions of online exam proctoring methods. The purpose of this exploratory study was to understand the perceptions of fully online students regarding types of proctoring at a large state…

In this paper, we describe two empirical studies that demonstrate the application and modeling of keystroke logs in writing assessments. We illustrate two different approaches of modeling differences in writing processes: analysis of mean differences in handcrafted theory-driven features and use of large language models to identify stable personal…

Multiple Choice Questions (MCQs) are a popular assessment method because they enable automated evaluation, flexible administration and use with huge groups. Despite these benefits, the manual construction of MCQs is challenging, time-consuming and error-prone. This is because each MCQ is comprised of a question called the "stem", a…

This study compares how the generative AI (GenAI) large language model (LLM) ChatGPT performs in grading university exams compared to human teachers. Aspects investigated include consistency, large discrepancies and length of answer. Implications for higher education, including the role of teachers and ethics, are also discussed. Three…

Driven by technological innovations, continuous digital expansion has transformed fundamentally the landscape of modern higher education, leading to discussions about evaluation techniques. The emergence of generative artificial intelligence raises questions about reliability and academic honesty regarding multiple-choice assessments in online…